@inproceedings{60e343771ed14f4fbf442a11e11edf18,
title = "The alignment problem for 3D ISAR imaging with real data",
abstract = "The alignment between the point-like 3D inverse synthetic aperture radar (ISAR) reconstruction produced by 3D ISAR imaging and model of the target is an essential issue in target recognition by 3D ISAR reconstruction, which contains coarse alignment and accurate alignment. The coarse alignment can be accomplished by principal component analysis (PCA). However, PCA may suffer from the 180-degree ambiguity problem due to the uncertainty of the orientations of principal components. In this paper, an effective and robust approach making use of the orthogonality among the principal components and k-d tree is proposed to address the ambiguity problem. The experimental results of the real measured data verify the validity of the proposed method.",
author = "Jinjian Cai and Marco Martorella and Quanhua Liu and Elisa Giusti and Zegang Ding",
note = "Publisher Copyright: {\textcopyright} VDE VERLAG GMBH . Berlin . Offenbach; 13th European Conference on Synthetic Aperture Radar, EUSAR 2021 ; Conference date: 29-03-2021 Through 01-04-2021",
year = "2021",
language = "English",
series = "Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "46--51",
booktitle = "EUSAR 2021 - 13th European Conference on Synthetic Aperture Radar, Proceedings",
address = "United States",
}